From Prompt to Production:Automating Brand-Safe Marketing Imagery with Text-to-Image Models
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866912904234663936 |
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| author | Atighehchian, Parmida Wang, Henry Kapustin, Andrei Lerner, Boris Jiang, Tiancheng Jensen, Taylor Sokhandan, Negin |
| author_facet | Atighehchian, Parmida Wang, Henry Kapustin, Andrei Lerner, Boris Jiang, Tiancheng Jensen, Taylor Sokhandan, Negin |
| contents | Text-to-image models have made significant strides, producing impressive results in generating images from textual descriptions. However, creating a scalable pipeline for deploying these models in production remains a challenge. Achieving the right balance between automation and human feedback is critical to maintain both scale and quality. While automation can handle large volumes, human oversight is still an essential component to ensure that the generated images meet the desired standards and are aligned with the creative vision. This paper presents a new pipeline that offers a fully automated, scalable solution for generating marketing images of commercial products using text-to-image models. The proposed system maintains the quality and fidelity of images, while also introducing sufficient creative variation to adhere to marketing guidelines. By streamlining this process, we ensure a seamless blend of efficiency and human oversight, achieving a $30.77\%$ increase in marketing object fidelity using DINOV2 and a $52.00\%$ increase in human preference over the generated outcome. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_13349 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | From Prompt to Production:Automating Brand-Safe Marketing Imagery with Text-to-Image Models Atighehchian, Parmida Wang, Henry Kapustin, Andrei Lerner, Boris Jiang, Tiancheng Jensen, Taylor Sokhandan, Negin Computer Vision and Pattern Recognition Artificial Intelligence I.2.10 Text-to-image models have made significant strides, producing impressive results in generating images from textual descriptions. However, creating a scalable pipeline for deploying these models in production remains a challenge. Achieving the right balance between automation and human feedback is critical to maintain both scale and quality. While automation can handle large volumes, human oversight is still an essential component to ensure that the generated images meet the desired standards and are aligned with the creative vision. This paper presents a new pipeline that offers a fully automated, scalable solution for generating marketing images of commercial products using text-to-image models. The proposed system maintains the quality and fidelity of images, while also introducing sufficient creative variation to adhere to marketing guidelines. By streamlining this process, we ensure a seamless blend of efficiency and human oversight, achieving a $30.77\%$ increase in marketing object fidelity using DINOV2 and a $52.00\%$ increase in human preference over the generated outcome. |
| title | From Prompt to Production:Automating Brand-Safe Marketing Imagery with Text-to-Image Models |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence I.2.10 |
| url | https://arxiv.org/abs/2602.13349 |